Conference proceeding
A Hierarchical Image Clustering Cosegmentation Framework
2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp 686-693
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Given the knowledge that the same or similar objects appear in a set of images, our goal is to simultaneously segment that object from the set of images. To solve this problem, known as the cosegmentation problem, we present a method based upon hierarchical clustering. Our framework first eliminates intra-class heterogeneity in a dataset by clustering similar images together into smaller groups. Then, from each image, our method extracts multiple levels of segmentation and creates connections between regions (e. g. superpixel) across levels to establish intra-image multi-scale constraints. Next we take advantage of the information available from other images in our group. We design and present an efficient method to create inter-image relationships, e. g. connections between image regions from one image to all other images in an image cluster. Given the intra & inter-image connections, we perform a segmentation of the group of images into foreground and background regions. Finally, we compare our segmentation accuracy to several other state-of-the-art segmentation methods on standard datasets, and also demonstrate the robustness of our method on real world data.
Metrics
Details
- Title
- A Hierarchical Image Clustering Cosegmentation Framework
- Creators
- Edward Kim - Lehigh UniversityHongsheng Li - Lehigh UniversityXiaolei Huang - Lehigh University
- Publication Details
- 2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp 686-693
- Series
- IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000309166200086
- Scopus ID
- 2-s2.0-84866639223
- Other Identifier
- 991021884588604721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Engineering, Electrical & Electronic